MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information. In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs. The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/. Supplementary data are available at Bioinformatics online.

Accumulating evidences indicate that circular RNAs (circRNAs) play crucial roles in a wide range of biological processes and are extensively involved in tumorigenesis and progression. The number of newly discovered circRNAs has increased dramatically in recent years, but the functions of vast majority of circRNAs are still elusive, and little effort has been attempted to discover disease-associated circRNAs on a large scale until now. With the advance of high-throughput technology, the increasing availability of omics data has provided an unprecedented opportunity for prioritizing candidate circRNAs for diseases by computational models, which will contribute to exploring the pathogenesis of complex diseases at the circRNA level and provide promising applications in disease diagnosis and treatment. Here we propose the assumption that circRNAs with similar functions are normally associated with similar diseases and vice versa, and develop an integrated computational framework called MRLDC to identify disease-associated circRNAs. To our knowledge, MRLDC is the first computational framework for prediction of circRNA-disease associations. By fully exploiting the experimentally validated associations between diseases and circRNAs, we firstly compute the Gaussian interaction profile kernel similarity for circRNAs and diseases, and then a heterogeneous circRNA-disease bilayer network is constructed by combining a circRNA similar network, a disease similar network and the known circRNA-disease associations. Subsequently, we develop a weighted low-rank approximation optimization algorithm with dual-manifold regularizations for predicting disease-associated circRNAs. Experiment results indicate that MRLDC can effectively identify disease circRNA candidates with high accuracy. In addition, case studies further demonstrate the ability of our method in discovering potential circRNA-disease associations.

Background: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.Results: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.Conclusions: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

MicroRNAs (miRNAs) play critical roles in many biological processes. Predicting the miRNA-disease associations will aid in deciphering the underlying pathogenesis of human polygenic diseases. However, existing in silico prediction methods typically utilize a single or limited data sources for disease-related miRNA prioritization and most of the methods are biased toward known miRNA-disease associations. Due to the insufficient number of experimentally validated interactions as well as no experimentally verified negative samples, obtaining remarkable performances is still challenging for these methods. In this paper, we present a semi-supervised method of Kronecker regularized least squares for predicting the potential or missing miRNA-disease associations (KRLSM). KRLSM integrates different omics data to assist various diseases or miRNAs with sparsely known associations to make predictions, and combines the disease space and miRNA space into a whole miRNA-disease space by Kronecker product. Finally, the semi-supervised classifier of regularized least squares is adopted to identify disease-related miRNAs. The experiment results demonstrate that the proposed method outperforms the other state-of-the-art approaches. In addition, case studies of several common diseases further indicate the effectiveness of KRLSM to identify potential miRNA-disease associations.

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.

A network consistency projection model for predicting novel circRNA–disease interactions.